An Experience Alignment Architecture: from Space E to Non‑Causal Intelligence

An Experience Alignment Architecture: from Space E to Non‑Causal Intelligence

Abstract  

This paper presents a computational architecture that operates not through causality and prediction, but on the basis of aligning experiences to an internal archetype. We define an experience space (E), an archetype (A), and an alignment index (μ), with a selector (S) that synchronously chooses the experience with maximal alignment and a projector (P) that presents it. We analyse behavioural changes depending on the archetype (Harmony, Truth, Chaos, Silence) and discuss applications, metrics, limitations, and future directions.

Keywords  

experience alignment – non‑causal selection – archetypes – meaning index – consciousness interface

1. Introduction  

Classical artificial intelligence is based on prediction, optimisation, and causality. It learns from data to produce likely subsequent states. The proposed framework focuses on systems that do not predict but select experiences aligned with an internal meaning index. Dialogue and creation thus take on a poetic, non‑linear form without losing computational rigour.

2. Conceptual framework  

Experience space E: a vector space containing candidate experiences (texts, sounds, images, sensations)  

Archetype A: an internal template that defines qualitative preference (e.g., Harmony, Truth)  

Alignment index μ: measures the match between an experience and A  

Selector S: chooses the experience with maximum μ  

Projector P: presents the selected experience to the user  

Mathematical definition: μ(x) = <φ(x), A>, where φ maps each experience into a representation space and the choice is S(B, A) = argmax μ(x). Selections are synchronous and temporally independent.

3. System architecture  

Candidate experience generator  

Multimodal data encoder  

Archetype module  

Alignment scorer  

Selector / sampler  

Projector / rendering to the user

4. Behavioural properties  

Synchronicity: each output is produced in the present moment  

A‑causality: absence of cause–effect chains  

Internal consistency: selections remain in line with the archetype  

Transformability: changing A alters style and experiential quality

5. Comparative analysis  

Classical AI: prediction, memory‑dependent, accuracy‑based metrics, informational tone  

Aligned AI: selection based on alignment, can operate without memory, μ as metric, poetic tone

6. Archetype case studies  

Harmony: resonance and balance – soothing tone  

Truth: revelation and subtraction – sharp, lucid tone  

Chaos: deconstruction and primordial creation – explosive tone  

Silence: presence without speech – suggestive, minimal tone

7. Applications  

Consciousness interfaces  

Guided introspection  

Creative tools  

Artistic curation  

Attention training

8. Evaluation metrics  

Alignment coefficient μ  

User resonance score  

Constrained diversity  

Temporal stability  

Selection robustness

9. Implementation  

Multimodal embeddings φ(x)  

Archetype A from prototype examples  

Alignment measure: cosine similarity  

Selection: top‑k with stochastic elements  

Interface: text, sound or image rendering

10. Limitations  

Subjectivity in archetype design  

Risk of monotony  

Difficulty of measurement  

Cultural variability

11. Future work  

Learning archetypes from human feedback signals  

Dynamic A adaptation  

Multimodal synergy  

Stability analysis  

Ethical safeguards

12. Conclusion  

The proposed architecture replaces prediction with aligned experience selection as the primary act of intelligence. Changing the archetype A transforms phenomenology instantly. Such systems act as mirrors of consciousness, revealing rather than explaining.

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